AI RESEARCH
What Information Matters? Graph Out-of-Distribution Detection via Tri-Component Information Decomposition
arXiv CS.LG
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ArXi:2605.13032v1 Announce Type: new Graph neural networks are widely used for node classification, but they remain vulnerable to out-of-distribution (OOD) shifts in node features and graph structure. Prior work established that methods trained with standard supervised learning (SL) objectives tend to capture spurious signals from either features and/or structure, leaving the model fragile under distributional changes.